Spaces:
Runtime error
Runtime error
File size: 33,578 Bytes
1bb1365 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 |
# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
# --------------------------------------------------------
# Dataset structure for flow
# --------------------------------------------------------
import json
import os
import os.path as osp
import pickle
import struct
from copy import deepcopy
import h5py
import numpy as np
import torch
from PIL import Image
from torch.utils import data
from .augmentor import FlowAugmentor
from .datasets_stereo import _read_img, _read_pfm, dataset_to_root, img_to_tensor
dataset_to_root = deepcopy(dataset_to_root)
dataset_to_root.update(
**{
"TartanAir": "./data/stereoflow/TartanAir",
"FlyingChairs": "./data/stereoflow/FlyingChairs/",
"FlyingThings": osp.join(dataset_to_root["SceneFlow"], "FlyingThings") + "/",
"MPISintel": "./data/stereoflow//MPI-Sintel/" + "/",
}
)
cache_dir = "./data/stereoflow/datasets_flow_cache/"
def flow_to_tensor(disp):
return torch.from_numpy(disp).float().permute(2, 0, 1)
class FlowDataset(data.Dataset):
def __init__(self, split, augmentor=False, crop_size=None, totensor=True):
self.split = split
if not augmentor:
assert crop_size is None
if crop_size is not None:
assert augmentor
self.crop_size = crop_size
self.augmentor_str = augmentor
self.augmentor = FlowAugmentor(crop_size) if augmentor else None
self.totensor = totensor
self.rmul = 1 # keep track of rmul
self.has_constant_resolution = True # whether the dataset has constant resolution or not (=> don't use batch_size>1 at test time)
self._prepare_data()
self._load_or_build_cache()
def prepare_data(self):
"""
to be defined for each dataset
"""
raise NotImplementedError
def __len__(self):
return len(
self.pairnames
) # each pairname is typically of the form (str, int1, int2)
def __getitem__(self, index):
pairname = self.pairnames[index]
# get filenames
img1name = self.pairname_to_img1name(pairname)
img2name = self.pairname_to_img2name(pairname)
flowname = (
self.pairname_to_flowname(pairname)
if self.pairname_to_flowname is not None
else None
)
# load images and disparities
img1 = _read_img(img1name)
img2 = _read_img(img2name)
flow = self.load_flow(flowname) if flowname is not None else None
# apply augmentations
if self.augmentor is not None:
img1, img2, flow = self.augmentor(img1, img2, flow, self.name)
if self.totensor:
img1 = img_to_tensor(img1)
img2 = img_to_tensor(img2)
if flow is not None:
flow = flow_to_tensor(flow)
else:
flow = torch.tensor(
[]
) # to allow dataloader batching with default collate_gn
pairname = str(
pairname
) # transform potential tuple to str to be able to batch it
return img1, img2, flow, pairname
def __rmul__(self, v):
self.rmul *= v
self.pairnames = v * self.pairnames
return self
def __str__(self):
return f"{self.__class__.__name__}_{self.split}"
def __repr__(self):
s = f"{self.__class__.__name__}(split={self.split}, augmentor={self.augmentor_str}, crop_size={str(self.crop_size)}, totensor={self.totensor})"
if self.rmul == 1:
s += f"\n\tnum pairs: {len(self.pairnames)}"
else:
s += f"\n\tnum pairs: {len(self.pairnames)} ({len(self.pairnames)//self.rmul}x{self.rmul})"
return s
def _set_root(self):
self.root = dataset_to_root[self.name]
assert os.path.isdir(
self.root
), f"could not find root directory for dataset {self.name}: {self.root}"
def _load_or_build_cache(self):
cache_file = osp.join(cache_dir, self.name + ".pkl")
if osp.isfile(cache_file):
with open(cache_file, "rb") as fid:
self.pairnames = pickle.load(fid)[self.split]
else:
tosave = self._build_cache()
os.makedirs(cache_dir, exist_ok=True)
with open(cache_file, "wb") as fid:
pickle.dump(tosave, fid)
self.pairnames = tosave[self.split]
class TartanAirDataset(FlowDataset):
def _prepare_data(self):
self.name = "TartanAir"
self._set_root()
assert self.split in ["train"]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root, pairname[0], "image_left/{:06d}_left.png".format(pairname[1])
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root, pairname[0], "image_left/{:06d}_left.png".format(pairname[2])
)
self.pairname_to_flowname = lambda pairname: osp.join(
self.root,
pairname[0],
"flow/{:06d}_{:06d}_flow.npy".format(pairname[1], pairname[2]),
)
self.pairname_to_str = lambda pairname: os.path.join(
pairname[0][pairname[0].find("/") + 1 :],
"{:06d}_{:06d}".format(pairname[1], pairname[2]),
)
self.load_flow = _read_numpy_flow
def _build_cache(self):
seqs = sorted(os.listdir(self.root))
pairs = [
(osp.join(s, s, difficulty, Pxxx), int(a[:6]), int(a[:6]) + 1)
for s in seqs
for difficulty in ["Easy", "Hard"]
for Pxxx in sorted(os.listdir(osp.join(self.root, s, s, difficulty)))
for a in sorted(
os.listdir(osp.join(self.root, s, s, difficulty, Pxxx, "image_left/"))
)[:-1]
]
assert len(pairs) == 306268, "incorrect parsing of pairs in TartanAir"
tosave = {"train": pairs}
return tosave
class FlyingChairsDataset(FlowDataset):
def _prepare_data(self):
self.name = "FlyingChairs"
self._set_root()
assert self.split in ["train", "val"]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root, "data", pairname + "_img1.ppm"
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root, "data", pairname + "_img2.ppm"
)
self.pairname_to_flowname = lambda pairname: osp.join(
self.root, "data", pairname + "_flow.flo"
)
self.pairname_to_str = lambda pairname: pairname
self.load_flow = _read_flo_file
def _build_cache(self):
split_file = osp.join(self.root, "chairs_split.txt")
split_list = np.loadtxt(split_file, dtype=np.int32)
trainpairs = ["{:05d}".format(i) for i in np.where(split_list == 1)[0] + 1]
valpairs = ["{:05d}".format(i) for i in np.where(split_list == 2)[0] + 1]
assert (
len(trainpairs) == 22232 and len(valpairs) == 640
), "incorrect parsing of pairs in MPI-Sintel"
tosave = {"train": trainpairs, "val": valpairs}
return tosave
class FlyingThingsDataset(FlowDataset):
def _prepare_data(self):
self.name = "FlyingThings"
self._set_root()
assert self.split in [
f"{set_}_{pass_}pass{camstr}"
for set_ in ["train", "test", "test1024"]
for camstr in ["", "_rightcam"]
for pass_ in ["clean", "final", "all"]
]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root,
f"frames_{pairname[3]}pass",
pairname[0].replace("into_future", "").replace("into_past", ""),
"{:04d}.png".format(pairname[1]),
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root,
f"frames_{pairname[3]}pass",
pairname[0].replace("into_future", "").replace("into_past", ""),
"{:04d}.png".format(pairname[2]),
)
self.pairname_to_flowname = lambda pairname: osp.join(
self.root,
"optical_flow",
pairname[0],
"OpticalFlowInto{f:s}_{i:04d}_{c:s}.pfm".format(
f="Future" if "future" in pairname[0] else "Past",
i=pairname[1],
c="L" if "left" in pairname[0] else "R",
),
)
self.pairname_to_str = lambda pairname: os.path.join(
pairname[3] + "pass",
pairname[0],
"Into{f:s}_{i:04d}_{c:s}".format(
f="Future" if "future" in pairname[0] else "Past",
i=pairname[1],
c="L" if "left" in pairname[0] else "R",
),
)
self.load_flow = _read_pfm_flow
def _build_cache(self):
tosave = {}
# train and test splits for the different passes
for set_ in ["train", "test"]:
sroot = osp.join(self.root, "optical_flow", set_.upper())
fname_to_i = lambda f: int(
f[len("OpticalFlowIntoFuture_") : -len("_L.pfm")]
)
pp = [
(osp.join(set_.upper(), d, s, "into_future/left"), fname_to_i(fname))
for d in sorted(os.listdir(sroot))
for s in sorted(os.listdir(osp.join(sroot, d)))
for fname in sorted(
os.listdir(osp.join(sroot, d, s, "into_future/left"))
)[:-1]
]
pairs = [(a, i, i + 1) for a, i in pp]
pairs += [(a.replace("into_future", "into_past"), i + 1, i) for a, i in pp]
assert (
len(pairs) == {"train": 40302, "test": 7866}[set_]
), "incorrect parsing of pairs Flying Things"
for cam in ["left", "right"]:
camstr = "" if cam == "left" else f"_{cam}cam"
for pass_ in ["final", "clean"]:
tosave[f"{set_}_{pass_}pass{camstr}"] = [
(a.replace("left", cam), i, j, pass_) for a, i, j in pairs
]
tosave[f"{set_}_allpass{camstr}"] = (
tosave[f"{set_}_cleanpass{camstr}"]
+ tosave[f"{set_}_finalpass{camstr}"]
)
# test1024: this is the same split as unimatch 'validation' split
# see https://github.com/autonomousvision/unimatch/blob/master/dataloader/flow/datasets.py#L229
test1024_nsamples = 1024
alltest_nsamples = len(tosave["test_cleanpass"]) # 7866
stride = alltest_nsamples // test1024_nsamples
remove = alltest_nsamples % test1024_nsamples
for cam in ["left", "right"]:
camstr = "" if cam == "left" else f"_{cam}cam"
for pass_ in ["final", "clean"]:
tosave[f"test1024_{pass_}pass{camstr}"] = sorted(
tosave[f"test_{pass_}pass{camstr}"]
)[:-remove][
::stride
] # warning, it was not sorted before
assert (
len(tosave["test1024_cleanpass"]) == 1024
), "incorrect parsing of pairs in Flying Things"
tosave[f"test1024_allpass{camstr}"] = (
tosave[f"test1024_cleanpass{camstr}"]
+ tosave[f"test1024_finalpass{camstr}"]
)
return tosave
class MPISintelDataset(FlowDataset):
def _prepare_data(self):
self.name = "MPISintel"
self._set_root()
assert self.split in [
s + "_" + p
for s in ["train", "test", "subval", "subtrain"]
for p in ["cleanpass", "finalpass", "allpass"]
]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root, pairname[0], "frame_{:04d}.png".format(pairname[1])
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root, pairname[0], "frame_{:04d}.png".format(pairname[1] + 1)
)
self.pairname_to_flowname = (
lambda pairname: None
if pairname[0].startswith("test/")
else osp.join(
self.root,
pairname[0].replace("/clean/", "/flow/").replace("/final/", "/flow/"),
"frame_{:04d}.flo".format(pairname[1]),
)
)
self.pairname_to_str = lambda pairname: osp.join(
pairname[0], "frame_{:04d}".format(pairname[1])
)
self.load_flow = _read_flo_file
def _build_cache(self):
trainseqs = sorted(os.listdir(self.root + "training/clean"))
trainpairs = [
(osp.join("training/clean", s), i)
for s in trainseqs
for i in range(1, len(os.listdir(self.root + "training/clean/" + s)))
]
subvalseqs = ["temple_2", "temple_3"]
subtrainseqs = [s for s in trainseqs if s not in subvalseqs]
subvalpairs = [(p, i) for p, i in trainpairs if any(s in p for s in subvalseqs)]
subtrainpairs = [
(p, i) for p, i in trainpairs if any(s in p for s in subtrainseqs)
]
testseqs = sorted(os.listdir(self.root + "test/clean"))
testpairs = [
(osp.join("test/clean", s), i)
for s in testseqs
for i in range(1, len(os.listdir(self.root + "test/clean/" + s)))
]
assert (
len(trainpairs) == 1041
and len(testpairs) == 552
and len(subvalpairs) == 98
and len(subtrainpairs) == 943
), "incorrect parsing of pairs in MPI-Sintel"
tosave = {}
tosave["train_cleanpass"] = trainpairs
tosave["test_cleanpass"] = testpairs
tosave["subval_cleanpass"] = subvalpairs
tosave["subtrain_cleanpass"] = subtrainpairs
for t in ["train", "test", "subval", "subtrain"]:
tosave[t + "_finalpass"] = [
(p.replace("/clean/", "/final/"), i)
for p, i in tosave[t + "_cleanpass"]
]
tosave[t + "_allpass"] = tosave[t + "_cleanpass"] + tosave[t + "_finalpass"]
return tosave
def submission_save_pairname(self, pairname, prediction, outdir, _time):
assert prediction.shape[2] == 2
outfile = os.path.join(
outdir, "submission", self.pairname_to_str(pairname) + ".flo"
)
os.makedirs(os.path.dirname(outfile), exist_ok=True)
writeFlowFile(prediction, outfile)
def finalize_submission(self, outdir):
assert self.split == "test_allpass"
bundle_exe = "/nfs/data/ffs-3d/datasets/StereoFlow/MPI-Sintel/bundler/linux-x64/bundler" # eg <bundle_exe> <path_to_results_for_clean> <path_to_results_for_final> <output/bundled.lzma>
if os.path.isfile(bundle_exe):
cmd = f'{bundle_exe} "{outdir}/submission/test/clean/" "{outdir}/submission/test/final" "{outdir}/submission/bundled.lzma"'
print(cmd)
os.system(cmd)
print(f'Done. Submission file at: "{outdir}/submission/bundled.lzma"')
else:
print("Could not find bundler executable for submission.")
print("Please download it and run:")
print(
f'<bundle_exe> "{outdir}/submission/test/clean/" "{outdir}/submission/test/final" "{outdir}/submission/bundled.lzma"'
)
class SpringDataset(FlowDataset):
def _prepare_data(self):
self.name = "Spring"
self._set_root()
assert self.split in ["train", "test", "subtrain", "subval"]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root,
pairname[0],
pairname[1],
"frame_" + pairname[3],
"frame_{:s}_{:04d}.png".format(pairname[3], pairname[4]),
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root,
pairname[0],
pairname[1],
"frame_" + pairname[3],
"frame_{:s}_{:04d}.png".format(
pairname[3], pairname[4] + (1 if pairname[2] == "FW" else -1)
),
)
self.pairname_to_flowname = (
lambda pairname: None
if pairname[0] == "test"
else osp.join(
self.root,
pairname[0],
pairname[1],
f"flow_{pairname[2]}_{pairname[3]}",
f"flow_{pairname[2]}_{pairname[3]}_{pairname[4]:04d}.flo5",
)
)
self.pairname_to_str = lambda pairname: osp.join(
pairname[0],
pairname[1],
f"flow_{pairname[2]}_{pairname[3]}",
f"flow_{pairname[2]}_{pairname[3]}_{pairname[4]:04d}",
)
self.load_flow = _read_hdf5_flow
def _build_cache(self):
# train
trainseqs = sorted(os.listdir(osp.join(self.root, "train")))
trainpairs = []
for leftright in ["left", "right"]:
for fwbw in ["FW", "BW"]:
trainpairs += [
(
"train",
s,
fwbw,
leftright,
int(f[len(f"flow_{fwbw}_{leftright}_") : -len(".flo5")]),
)
for s in trainseqs
for f in sorted(
os.listdir(
osp.join(self.root, "train", s, f"flow_{fwbw}_{leftright}")
)
)
]
# test
testseqs = sorted(os.listdir(osp.join(self.root, "test")))
testpairs = []
for leftright in ["left", "right"]:
testpairs += [
(
"test",
s,
"FW",
leftright,
int(f[len(f"frame_{leftright}_") : -len(".png")]),
)
for s in testseqs
for f in sorted(
os.listdir(osp.join(self.root, "test", s, f"frame_{leftright}"))
)[:-1]
]
testpairs += [
(
"test",
s,
"BW",
leftright,
int(f[len(f"frame_{leftright}_") : -len(".png")]) + 1,
)
for s in testseqs
for f in sorted(
os.listdir(osp.join(self.root, "test", s, f"frame_{leftright}"))
)[:-1]
]
# subtrain / subval
subtrainpairs = [p for p in trainpairs if p[1] != "0041"]
subvalpairs = [p for p in trainpairs if p[1] == "0041"]
assert (
len(trainpairs) == 19852
and len(testpairs) == 3960
and len(subtrainpairs) == 19472
and len(subvalpairs) == 380
), "incorrect parsing of pairs in Spring"
tosave = {
"train": trainpairs,
"test": testpairs,
"subtrain": subtrainpairs,
"subval": subvalpairs,
}
return tosave
def submission_save_pairname(self, pairname, prediction, outdir, time):
assert prediction.ndim == 3
assert prediction.shape[2] == 2
assert prediction.dtype == np.float32
outfile = osp.join(
outdir,
pairname[0],
pairname[1],
f"flow_{pairname[2]}_{pairname[3]}",
f"flow_{pairname[2]}_{pairname[3]}_{pairname[4]:04d}.flo5",
)
os.makedirs(os.path.dirname(outfile), exist_ok=True)
writeFlo5File(prediction, outfile)
def finalize_submission(self, outdir):
assert self.split == "test"
exe = "{self.root}/flow_subsampling"
if os.path.isfile(exe):
cmd = f'cd "{outdir}/test"; {exe} .'
print(cmd)
os.system(cmd)
print(f"Done. Submission file at {outdir}/test/flow_submission.hdf5")
else:
print("Could not find flow_subsampling executable for submission.")
print("Please download it and run:")
print(f'cd "{outdir}/test"; <flow_subsampling_exe> .')
class Kitti12Dataset(FlowDataset):
def _prepare_data(self):
self.name = "Kitti12"
self._set_root()
assert self.split in ["train", "test"]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root, pairname + "_10.png"
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root, pairname + "_11.png"
)
self.pairname_to_flowname = (
None
if self.split == "test"
else lambda pairname: osp.join(
self.root, pairname.replace("/colored_0/", "/flow_occ/") + "_10.png"
)
)
self.pairname_to_str = lambda pairname: pairname.replace("/colored_0/", "/")
self.load_flow = _read_kitti_flow
def _build_cache(self):
trainseqs = ["training/colored_0/%06d" % (i) for i in range(194)]
testseqs = ["testing/colored_0/%06d" % (i) for i in range(195)]
assert (
len(trainseqs) == 194 and len(testseqs) == 195
), "incorrect parsing of pairs in Kitti12"
tosave = {"train": trainseqs, "test": testseqs}
return tosave
def submission_save_pairname(self, pairname, prediction, outdir, time):
assert prediction.ndim == 3
assert prediction.shape[2] == 2
outfile = os.path.join(outdir, pairname.split("/")[-1] + "_10.png")
os.makedirs(os.path.dirname(outfile), exist_ok=True)
writeFlowKitti(outfile, prediction)
def finalize_submission(self, outdir):
assert self.split == "test"
cmd = f'cd {outdir}/; zip -r "kitti12_flow_results.zip" .'
print(cmd)
os.system(cmd)
print(f"Done. Submission file at {outdir}/kitti12_flow_results.zip")
class Kitti15Dataset(FlowDataset):
def _prepare_data(self):
self.name = "Kitti15"
self._set_root()
assert self.split in ["train", "subtrain", "subval", "test"]
self.pairname_to_img1name = lambda pairname: osp.join(
self.root, pairname + "_10.png"
)
self.pairname_to_img2name = lambda pairname: osp.join(
self.root, pairname + "_11.png"
)
self.pairname_to_flowname = (
None
if self.split == "test"
else lambda pairname: osp.join(
self.root, pairname.replace("/image_2/", "/flow_occ/") + "_10.png"
)
)
self.pairname_to_str = lambda pairname: pairname.replace("/image_2/", "/")
self.load_flow = _read_kitti_flow
def _build_cache(self):
trainseqs = ["training/image_2/%06d" % (i) for i in range(200)]
subtrainseqs = trainseqs[:-10]
subvalseqs = trainseqs[-10:]
testseqs = ["testing/image_2/%06d" % (i) for i in range(200)]
assert (
len(trainseqs) == 200
and len(subtrainseqs) == 190
and len(subvalseqs) == 10
and len(testseqs) == 200
), "incorrect parsing of pairs in Kitti15"
tosave = {
"train": trainseqs,
"subtrain": subtrainseqs,
"subval": subvalseqs,
"test": testseqs,
}
return tosave
def submission_save_pairname(self, pairname, prediction, outdir, time):
assert prediction.ndim == 3
assert prediction.shape[2] == 2
outfile = os.path.join(outdir, "flow", pairname.split("/")[-1] + "_10.png")
os.makedirs(os.path.dirname(outfile), exist_ok=True)
writeFlowKitti(outfile, prediction)
def finalize_submission(self, outdir):
assert self.split == "test"
cmd = f'cd {outdir}/; zip -r "kitti15_flow_results.zip" flow'
print(cmd)
os.system(cmd)
print(f"Done. Submission file at {outdir}/kitti15_flow_results.zip")
import cv2
def _read_numpy_flow(filename):
return np.load(filename)
def _read_pfm_flow(filename):
f, _ = _read_pfm(filename)
assert np.all(f[:, :, 2] == 0.0)
return np.ascontiguousarray(f[:, :, :2])
TAG_FLOAT = 202021.25 # tag to check the sanity of the file
TAG_STRING = "PIEH" # string containing the tag
MIN_WIDTH = 1
MAX_WIDTH = 99999
MIN_HEIGHT = 1
MAX_HEIGHT = 99999
def readFlowFile(filename):
"""
readFlowFile(<FILENAME>) reads a flow file <FILENAME> into a 2-band np.array.
if <FILENAME> does not exist, an IOError is raised.
if <FILENAME> does not finish by '.flo' or the tag, the width, the height or the file's size is illegal, an Expcetion is raised.
---- PARAMETERS ----
filename: string containg the name of the file to read a flow
---- OUTPUTS ----
a np.array of dimension (height x width x 2) containing the flow of type 'float32'
"""
# check filename
if not filename.endswith(".flo"):
raise Exception(
"readFlowFile({:s}): filename must finish with '.flo'".format(filename)
)
# open the file and read it
with open(filename, "rb") as f:
# check tag
tag = struct.unpack("f", f.read(4))[0]
if tag != TAG_FLOAT:
raise Exception("flow_utils.readFlowFile({:s}): wrong tag".format(filename))
# read dimension
w, h = struct.unpack("ii", f.read(8))
if w < MIN_WIDTH or w > MAX_WIDTH:
raise Exception(
"flow_utils.readFlowFile({:s}: illegal width {:d}".format(filename, w)
)
if h < MIN_HEIGHT or h > MAX_HEIGHT:
raise Exception(
"flow_utils.readFlowFile({:s}: illegal height {:d}".format(filename, h)
)
flow = np.fromfile(f, "float32")
if not flow.shape == (h * w * 2,):
raise Exception(
"flow_utils.readFlowFile({:s}: illegal size of the file".format(
filename
)
)
flow.shape = (h, w, 2)
return flow
def writeFlowFile(flow, filename):
"""
writeFlowFile(flow,<FILENAME>) write flow to the file <FILENAME>.
if <FILENAME> does not exist, an IOError is raised.
if <FILENAME> does not finish with '.flo' or the flow has not 2 bands, an Exception is raised.
---- PARAMETERS ----
flow: np.array of dimension (height x width x 2) containing the flow to write
filename: string containg the name of the file to write a flow
"""
# check filename
if not filename.endswith(".flo"):
raise Exception(
"flow_utils.writeFlowFile(<flow>,{:s}): filename must finish with '.flo'".format(
filename
)
)
if not flow.shape[2:] == (2,):
raise Exception(
"flow_utils.writeFlowFile(<flow>,{:s}): <flow> must have 2 bands".format(
filename
)
)
# open the file and write it
with open(filename, "wb") as f:
# write TAG
f.write(TAG_STRING.encode("utf-8"))
# write dimension
f.write(struct.pack("ii", flow.shape[1], flow.shape[0]))
# write the flow
flow.astype(np.float32).tofile(f)
_read_flo_file = readFlowFile
def _read_kitti_flow(filename):
flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
flow = flow[:, :, ::-1].astype(np.float32)
valid = flow[:, :, 2] > 0
flow = flow[:, :, :2]
flow = (flow - 2**15) / 64.0
flow[~valid, 0] = np.inf
flow[~valid, 1] = np.inf
return flow
_read_hd1k_flow = _read_kitti_flow
def writeFlowKitti(filename, uv):
uv = 64.0 * uv + 2**15
valid = np.ones([uv.shape[0], uv.shape[1], 1])
uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16)
cv2.imwrite(filename, uv[..., ::-1])
def writeFlo5File(flow, filename):
with h5py.File(filename, "w") as f:
f.create_dataset("flow", data=flow, compression="gzip", compression_opts=5)
def _read_hdf5_flow(filename):
flow = np.asarray(h5py.File(filename)["flow"])
flow[np.isnan(flow)] = np.inf # make invalid values as +inf
return flow.astype(np.float32)
# flow visualization
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
UNKNOWN_THRESH = 1e9
def colorTest():
"""
flow_utils.colorTest(): display an example of image showing the color encoding scheme
"""
import matplotlib.pylab as plt
truerange = 1
h, w = 151, 151
trange = truerange * 1.04
s2 = round(h / 2)
x, y = np.meshgrid(range(w), range(h))
u = x * trange / s2 - trange
v = y * trange / s2 - trange
img = _computeColor(
np.concatenate((u[:, :, np.newaxis], v[:, :, np.newaxis]), 2)
/ trange
/ np.sqrt(2)
)
plt.imshow(img)
plt.axis("off")
plt.axhline(round(h / 2), color="k")
plt.axvline(round(w / 2), color="k")
def flowToColor(flow, maxflow=None, maxmaxflow=None, saturate=False):
"""
flow_utils.flowToColor(flow): return a color code flow field, normalized based on the maximum l2-norm of the flow
flow_utils.flowToColor(flow,maxflow): return a color code flow field, normalized by maxflow
---- PARAMETERS ----
flow: flow to display of shape (height x width x 2)
maxflow (default:None): if given, normalize the flow by its value, otherwise by the flow norm
maxmaxflow (default:None): if given, normalize the flow by the max of its value and the flow norm
---- OUTPUT ----
an np.array of shape (height x width x 3) of type uint8 containing a color code of the flow
"""
h, w, n = flow.shape
# check size of flow
assert n == 2, "flow_utils.flowToColor(flow): flow must have 2 bands"
# fix unknown flow
unknown_idx = np.max(np.abs(flow), 2) > UNKNOWN_THRESH
flow[unknown_idx] = 0.0
# compute max flow if needed
if maxflow is None:
maxflow = flowMaxNorm(flow)
if maxmaxflow is not None:
maxflow = min(maxmaxflow, maxflow)
# normalize flow
eps = np.spacing(1) # minimum positive float value to avoid division by 0
# compute the flow
img = _computeColor(flow / (maxflow + eps), saturate=saturate)
# put black pixels in unknown location
img[np.tile(unknown_idx[:, :, np.newaxis], [1, 1, 3])] = 0.0
return img
def flowMaxNorm(flow):
"""
flow_utils.flowMaxNorm(flow): return the maximum of the l2-norm of the given flow
---- PARAMETERS ----
flow: the flow
---- OUTPUT ----
a float containing the maximum of the l2-norm of the flow
"""
return np.max(np.sqrt(np.sum(np.square(flow), 2)))
def _computeColor(flow, saturate=True):
"""
flow_utils._computeColor(flow): compute color codes for the flow field flow
---- PARAMETERS ----
flow: np.array of dimension (height x width x 2) containing the flow to display
---- OUTPUTS ----
an np.array of dimension (height x width x 3) containing the color conversion of the flow
"""
# set nan to 0
nanidx = np.isnan(flow[:, :, 0])
flow[nanidx] = 0.0
# colorwheel
ncols = RY + YG + GC + CB + BM + MR
nchans = 3
colorwheel = np.zeros((ncols, nchans), "uint8")
col = 0
# RY
colorwheel[:RY, 0] = 255
colorwheel[:RY, 1] = [(255 * i) // RY for i in range(RY)]
col += RY
# YG
colorwheel[col : col + YG, 0] = [255 - (255 * i) // YG for i in range(YG)]
colorwheel[col : col + YG, 1] = 255
col += YG
# GC
colorwheel[col : col + GC, 1] = 255
colorwheel[col : col + GC, 2] = [(255 * i) // GC for i in range(GC)]
col += GC
# CB
colorwheel[col : col + CB, 1] = [255 - (255 * i) // CB for i in range(CB)]
colorwheel[col : col + CB, 2] = 255
col += CB
# BM
colorwheel[col : col + BM, 0] = [(255 * i) // BM for i in range(BM)]
colorwheel[col : col + BM, 2] = 255
col += BM
# MR
colorwheel[col : col + MR, 0] = 255
colorwheel[col : col + MR, 2] = [255 - (255 * i) // MR for i in range(MR)]
# compute utility variables
rad = np.sqrt(np.sum(np.square(flow), 2)) # magnitude
a = np.arctan2(-flow[:, :, 1], -flow[:, :, 0]) / np.pi # angle
fk = (a + 1) / 2 * (ncols - 1) # map [-1,1] to [0,ncols-1]
k0 = np.floor(fk).astype("int")
k1 = k0 + 1
k1[k1 == ncols] = 0
f = fk - k0
if not saturate:
rad = np.minimum(rad, 1)
# compute the image
img = np.zeros((flow.shape[0], flow.shape[1], nchans), "uint8")
for i in range(nchans):
tmp = colorwheel[:, i].astype("float")
col0 = tmp[k0] / 255
col1 = tmp[k1] / 255
col = (1 - f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1 - rad[idx] * (1 - col[idx]) # increase saturation with radius
col[~idx] *= 0.75 # out of range
img[:, :, i] = (255 * col * (1 - nanidx.astype("float"))).astype("uint8")
return img
# flow dataset getter
def get_train_dataset_flow(dataset_str, augmentor=True, crop_size=None):
dataset_str = dataset_str.replace("(", "Dataset(")
if augmentor:
dataset_str = dataset_str.replace(")", ", augmentor=True)")
if crop_size is not None:
dataset_str = dataset_str.replace(
")", ", crop_size={:s})".format(str(crop_size))
)
return eval(dataset_str)
def get_test_datasets_flow(dataset_str):
dataset_str = dataset_str.replace("(", "Dataset(")
return [eval(s) for s in dataset_str.split("+")]
|